Nonlinear programming

Physics

Channel (broadcasting)

Nonlinear system

Computer network

Mathematics

Wireless

Agronomy

Base station

Assignment problem

Biology

Algorithm

Mathematical optimization

Geodesy

Channel allocation schemes

Optimization problem

Fractional programming

Benchmark (surveying)

Cellular network

Power (physics)

Controller (irrigation)

Geography

Hungarian algorithm

Computer science

Resource management (computing)

Quality of service

Inner loop

Quantum mechanics

Resource allocation

Power control

Telecommunications

IEEE Transactions on Vehicular Technology

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods.

Energy-Efficient Resource Allocation for Secure D2D Communications Underlaying UAV-Enabled Networks